import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo


__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152']


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


# basic 3x3 conv wrapper
def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


# Naive residual block for Renset18/34
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        # stride/2 maybe applied on conv1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        # Conv + BatchNorm + RelU
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        # Downsample: feature Map size/2 || Channel increase
        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


# BottleNeck Residual block for Renset50/101/152
class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        # 1x1 conv
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        # 3x3 conv
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        # 1x1 conv
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)

        self.fc = nn.Linear(512 * block.expansion, num_classes)
        self.out_num_features = 512 * block.expansion

        # kaiming weight normal after default init
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    # construct layer/stage conv2_x,conv3_x,conv4_x,conv5_x
    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        # when to need downsample
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        # inplanes expand for next block
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        global_feat = self.avgpool(x)

        return x, global_feat


def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model


def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model


def resnet101(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model
# vim: ts=4 sw=4 sts=4 expandtab


class ADM_ExtraBlock(nn.Module):
    expansion = 1

    def __init__(
            self, inplanes, planes,
            kernel_size=3, stride=1, downsample=None
    ):
        super(ADM_ExtraBlock, self).__init__()
        # stride/2 maybe applied on conv1
        self.conv1 = nn.Conv2d(
            inplanes, planes, kernel_size=kernel_size, stride=stride)

        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        # Conv + BatchNorm + RelU
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        # Downsample: feature Map size/2 || Channel increase
        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ADM_EndBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, kernel_size=3, stride=1):
        super(ADM_EndBlock, self).__init__()
        # stride/2 maybe applied on conv1
        self.conv1 = nn.Conv2d(
            inplanes, planes, kernel_size=kernel_size, stride=stride)

        self.relu = nn.ReLU(inplace=True)
        # Conv + BatchNorm + RelU
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=1, stride=1)

        self.downsample = nn.Conv2d(
            inplanes, planes, kernel_size=kernel_size, stride=stride
        )

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.relu(out)

        out = self.conv2(out)

        out += self.downsample(residual)
        out = self.relu(out)

        return out


class Multi_scale_Detection_Module(nn.Module):

    def __init__(
            self, inplanes, class_num=2,
            width_hight_ratios=2, extra_layers=None
    ):
        super(Multi_scale_Detection_Module, self).__init__()

        # Multi-scale Detection Module.

        multi_scale_detector = list()
        multi_scale_classifier = list()

        for extra_block in extra_layers:
            ks = 3 if extra_block != ADM_EndBlock else 1
            pad = 1 if extra_block != ADM_EndBlock else 0

            multi_scale_classifier.append(
                nn.Conv2d(
                    inplanes, width_hight_ratios*class_num,
                    kernel_size=ks, stride=1, padding=pad
                )
            )

            multi_scale_detector.append(
                nn.Conv2d(
                    inplanes, width_hight_ratios*4,
                    kernel_size=ks, stride=1, padding=pad
                )
            )

        self.ms_dets = nn.ModuleList(multi_scale_detector)
        self.ms_cls = nn.ModuleList(multi_scale_classifier)

    def forward(self, x):
        confidence, location = list(), list()
        for (feat, detector, classifier) in zip(x, self.ms_dets, self.ms_cls):
            location.append(detector(feat).permute(0, 2, 3, 1).contiguous())
            confidence.append(classifier(feat).permute(0, 2, 3, 1).contiguous())

        confidence = torch.cat([o.view(o.size(0), -1) for o in confidence], 1)
        location = torch.cat([o.view(o.size(0), -1) for o in location], 1)

        return location, confidence


class Artifact_Detection_Module(nn.Module):

    def __init__(
            self, inplanes, blocks=1, class_num=2,
            width_hight_ratios=2, extra_layers=None,
    ):

        super(Artifact_Detection_Module, self).__init__()

        # Artifact Detection Module Extra Layers.

        self.cls_num = class_num
        self.inplanes = inplanes

        adm_extra_layers = list()

        if extra_layers is None:
            extra_layers = [ADM_ExtraBlock] * 3 + [ADM_EndBlock]

        for i, extra_block in enumerate(extra_layers):
            ks = 3 if i else 1
            if extra_block != ADM_EndBlock:
                adm_extra_layers.append(
                    self._make_layer(
                        extra_block, inplanes,
                        blocks=blocks, kernel_size=ks, stride=1
                    )
                )
            else:
                adm_extra_layers.append(extra_block(inplanes, inplanes))

        self.adm_extra_layers = nn.ModuleList(adm_extra_layers)

        self.multi_scale_detection_module = Multi_scale_Detection_Module(
            inplanes, extra_layers=extra_layers
        )

    def _make_layer(self, block, planes, blocks, kernel_size, stride=1):
        downsample = nn.Sequential(
            nn.Conv2d(self.inplanes, planes * block.expansion,
                      kernel_size=kernel_size, stride=stride, bias=False),
            nn.BatchNorm2d(planes * block.expansion)
        )

        layers = []
        layers.append(block(
            self.inplanes, planes * block.expansion, kernel_size=kernel_size,
            stride=stride, downsample=downsample))
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes * block.expansion, ))

        return nn.Sequential(*layers)

    def forward(self, x):
        bs = x.size(0)
        adm_feats = list()

        for adm_layer in self.adm_extra_layers:
            x = adm_layer(x)
            adm_feats.append(x)

        location, confidence = self.multi_scale_detection_module(adm_feats)

        location = location.view(bs, -1, 4)
        confidence = confidence.view(bs, -1, self.cls_num)

        adm_final_feat = adm_feats[-1]

        return location, confidence, adm_final_feat

# vim: ts=4 sw=4 sts=4 expandtab


class CADDM(nn.Module):

    def __init__(self, num_classes=2, backbone='resnet34'):
        super(CADDM, self).__init__()

        self.num_classes = num_classes
        self.backbone = backbone


        self.base_model = resnet34(pretrained=True)


        self.inplanes = self.base_model.out_num_features

        self.adm = Artifact_Detection_Module(self.inplanes)

        self.fc = nn.Linear(self.inplanes, num_classes)

        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x):
        batch_num = x.size(0)

        x, global_feat = self.base_model(x)
        

        # location result, confidence of each anchor, final feature map of adm.
        loc, cof, adm_final_feat = self.adm(x)

        final_cls_feat = global_feat + adm_final_feat
        final_cls = self.fc(final_cls_feat.view(batch_num, -1))

        if self.training:
            return final_cls
        return self.softmax(final_cls)

# vim: ts=4 sw=4 sts=4 expandtab